File size: 2,369 Bytes
032a961 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 | #!/usr/bin/env python3
"""Probe: sharp sparse input drive -> token-discriminative spike patterns."""
import sys
import numpy as np
sys.path.insert(0, "/home/z/my-project/download/spira")
from spira import SPIRA, SPIRAConfig
from spira.spiking import SpikingCore
data = open("/home/z/my-project/data/train.bin", "rb").read()
cfg = SPIRAConfig(snn_tokens=40_000, am_tokens=1000, epochs=1)
model = SPIRA.build(data, cfg, log=lambda m: None)
ids = model._ids_train
N = 40_000
neurons = SPIRA.input_neurons_stream(ids[:N], cfg.d_in, cfg.k_in)
cols = model.cols_of(ids[: N + 1])
cov = (cols[1:] != cfg.vocab_size)[: N - 1].mean()
print(f"coverage: {cov:.3f}")
variants = [
# (w_in_scale, in_fanout, w_rec_scale, rec_fanout, inh_frac)
(3.0, 8, 0.35, 48, 0.50),
(2.5, 12, 0.35, 48, 0.50),
(4.0, 8, 0.35, 48, 0.50),
(3.0, 8, 0.25, 24, 0.50),
(5.0, 6, 0.35, 48, 0.50),
]
def pattern_discrimination(core, neurons, n=400, stride=17):
"""Mean Jaccard overlap of spike patterns for tokens at distant positions
with DIFFERENT patch ids; low overlap = high token information."""
core.reset()
pats = []
id_seen = []
for t in range(0, n * stride, stride):
core.step(neurons[t])
pats.append(set(np.nonzero(core.s)[0].tolist()))
id_seen.append(int(neurons[t][0])) # proxy: first neuron differs
overlaps = []
for i in range(len(pats) - 1):
for j in range(i + 1, min(i + 5, len(pats))):
a, b = pats[i], pats[j]
if a or b:
overlaps.append(len(a & b) / max(len(a | b), 1))
return float(np.mean(overlaps))
for w_in, fan, w_rec, recf, inh in variants:
core = SpikingCore(n_out=cfg.vocab_size + 1, seed=cfg.seed,
w_in_scale=w_in, in_fanout=fan,
w_rec_scale=w_rec, rec_fanout=recf, inh_frac=inh,
eta_r=0.002)
core.reset()
sp = 0
for t in range(2000):
core.step(neurons[t])
sp += int(core.s.sum())
rate = sp / 2000 / core.n_res
jac = pattern_discrimination(core, neurons)
res = core.train_pass(neurons[: N - 1], cols[1:N], log_every=20_000, log_fn=lambda d: None)
print(f"w_in={w_in:.1f} fan={fan} w_rec={w_rec:.2f} recf={recf} | rate={rate:.3f} "
f"jac={jac:.3f} overall={res['accuracy']:.4f} in-vocab~={res['accuracy']/cov:.4f}")
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